arXiv subjects

Fabian Braesemann

Publications and source records attributed to Fabian Braesemann.

9 recordsLinked to original sources

arXiv2026

The economics of global personality diversity

This study explores the relationship between personality diversity and national economic performance, introducing the Global Personality Diversity Index ($Ψ$-GPDI) as a novel metric. Leveraging a dataset of 760,242 individuals across 135 countries, we quantify within-country diversity based on the Big Five personality traits. Our findings reveal that personality diversity accounts for 19.9% of the variance in GDP per person employed and provides an additional 5.7% explanatory power beyond institutional quality and immigrant diversity, underscoring its unique contribution to economic vitality. Through multi-factor analysis, we demonstrate how personality diversity complements existing economic frameworks, offering actionable insights for policymakers seeking to enhance innovation, productivity, and resilience. This research positions psychological diversity as a critical yet under explored factor in driving economic growth, bridging the fields of psychology and economics.

econ.GN↗

arXiv2023

How to foster innovation in the social sciences? Qualitative evidence from focus group workshops at Oxford University

This report addresses challenges and opportunities for innovation in the social sciences at the University of Oxford. It summarises findings from two focus group workshops with innovation experts from the University ecosystem. Experts included successful social science entrepreneurs and professional service staff from the University. The workshops focused on four different dimensions related to innovative activities and commercialisation. The findings show several challenges at the institutional and individual level, together with features of the social scientific discipline that impede more innovation in the social sciences. Based on identifying these challenges, we present potential solutions and ways forward identified in the focus group discussions to foster social science innovation. The report aims to illustrate the potential of innovation and commercialisation of social scientific research for both researchers and the university.

econ.GN↗

arXiv2023

The Science of Startups: The Impact of Founder Personalities on Company Success

Startup companies solve many of today's most complex and challenging scientific, technical and social problems, such as the decarbonisation of the economy, air pollution, and the development of novel life-saving vaccines. Startups are a vital source of social, scientific and economic innovation, yet the most innovative are also the least likely to survive. The probability of success of startups has been shown to relate to several firm-level factors such as industry, location and the economy of the day. Still, attention has increasingly considered internal factors relating to the firm's founding team, including their previous experiences and failures, their centrality in a global network of other founders and investors as well as the team's size. The effects of founders' personalities on the success of new ventures are mainly unknown. Here we show that founder personality traits are a significant feature of a firm's ultimate success. We draw upon detailed data about the success of a large-scale global sample of startups. We found that the Big 5 personality traits of startup founders across 30 dimensions significantly differed from that of the population at large. Key personality facets that distinguish successful entrepreneurs include a preference for variety, novelty and starting new things (openness to adventure), like being the centre of attention (lower levels of modesty) and being exuberant (higher activity levels). However, we do not find one "Founder-type" personality; instead, six different personality types appear, with startups founded by a "Hipster, Hacker and Hustler" being twice as likely to succeed. Our results also demonstrate the benefits of larger, personality-diverse teams in startups, which has the potential to be extended through further research into other team settings within business, government and research.

econ.GN↗

arXiv2022

The global polarisation of remote work

The Covid-19 pandemic has led to the rise of remote work with consequences for the global division of work. Remote work could connect labour markets, but it could also increase spatial polarisation. However, our understanding of the geographies of remote work is limited. Specifically, does remote work bring jobs to rural areas or is it concentrating in large cities, and how do skill requirements affect competition for jobs and wages? We use data from a fully remote labour market - an online labour platform - to show that remote work is polarised along three dimensions. First, countries are globally divided: North American, European, and South Asian remote workers attract most jobs, while many Global South countries participate only marginally. Secondly, remote jobs are pulled to urban regions; rural areas fall behind. Thirdly, remote work is polarised along the skill axis: workers with in-demand skills attract profitable jobs, while others face intense competition and obtain low wages. The findings suggest that remote work is shaped by agglomerative forces, which are deepening the gap between urban and rural areas. To make remote work an effective tool for rural development, it needs to be embedded in local skill-building and labour market programmes.

econ.GN↗

arXiv2022

Data Science vs Putin: How much does each of us pay for Putin's war?

Putin's Ukraine war has caused gas prices to skyrocket. Because of Europe's dependence on Russian gas supplies, we all pay significantly more for heating, involuntarily helping to fund Russia's war against Ukraine. Based on an analysis of real-time gas price data, we present a calculation that estimates every household's financial contribution for heating paid to Russian gas suppliers daily at current prices - six euros per household and day. We show ways everyone can save energy and help reduce the dependency on Russian gas supply.

econ.GN↗

arXiv2020

A Mixed-Method Landscape Analysis of SME-focused B2B Platforms in Germany

Digital platforms offer vast potential for increased value creation and innovation, especially through cross-organizational data sharing. It appears that SMEs in Germany are currently hesitant or unable to create their own platforms. To get a holistic overview of the structure of the German SME-focused platform landscape (that is platforms that are led by or targeting SMEs), we applied a mixed method approach of traditional desk research and a quantitative analysis. The study identified large geographical disparity along the borders of the new and old German federal states, and overall fewer platform ventures by SMEs, rather than large companies and startups. Platform ventures for SMEs are more likely set up as partnerships. We indicate that high capital intensity might be a reason for that.

econ.GN↗

arXiv2020

Social media self-branding and success: Quantitative evidence from a model competition

Thanks to the availability of large online data sets, it has become possible to quantify success in different fields of human endeavour. The study presented here contributes to this literature in evaluating the effect of social media activity, as a means of 'self-branding', to increase the chances of models being elected for the Playboy Magazine's Playmate of the Year award. We hypothesise that candidates who actively manage their Instagram accounts can increase their likelihood to win the award: they use social media to gain more followers, who then might vote for them in the award polls. The findings indicate that social media activity actually has predictive capacity to estimate the outcome of the award. We find evidence that candidates who manage their social media accounts more actively than other candidates have a higher probability to become Playmate of the Year. The findings underline the benefits of social media

cs.SI↗

arXiv2020

The CoRisk-Index: A data-mining approach to identify industry-specific risk assessments related to COVID-19 in real-time

While the coronavirus spreads, governments are attempting to reduce contagion rates at the expense of negative economic effects. Market expectations plummeted, foreshadowing the risk of a global economic crisis and mass unemployment. Governments provide huge financial aid programmes to mitigate the economic shocks. To achieve higher effectiveness with such policy measures, it is key to identify the industries that are most in need of support. In this study, we introduce a data-mining approach to measure industry-specific risks related to COVID-19. We examine company risk reports filed to the U.S. Securities and Exchange Commission (SEC). This alternative data set can complement more traditional economic indicators in times of the fast-evolving crisis as it allows for a real-time analysis of risk assessments. Preliminary findings suggest that the companies' awareness towards corona-related business risks is ahead of the overall stock market developments. Our approach allows to distinguish the industries by their risk awareness towards COVID-19. Based on natural language processing, we identify corona-related risk topics and their perceived relevance for different industries. The preliminary findings are summarised as an up-to-date online index. The CoRisk-Index tracks the industry-specific risk assessments related to the crisis, as it spreads through the economy. The tracking tool is updated weekly. It could provide relevant empirical data to inform models on the economic effects of the crisis. Such complementary empirical information could ultimately help policymakers to effectively target financial support in order to mitigate the economic shocks of the crisis.

econ.GN↗

arXiv2020

Mining the Automotive Industry: A Network Analysis of Corporate Positioning and Technological Trends

The digital transformation is driving revolutionary innovations and new market entrants threaten established sectors of the economy such as the automotive industry. Following the need for monitoring shifting industries, we present a network-centred analysis of car manufacturer web pages. Solely exploiting publicly-available information, we construct large networks from web pages and hyperlinks. The network properties disclose the internal corporate positioning of the three largest automotive manufacturers, Toyota, Volkswagen and Hyundai with respect to innovative trends and their international outlook. We tag web pages concerned with topics like e-mobility and environment or autonomous driving, and investigate their relevance in the network. Sentiment analysis on individual web pages uncovers a relationship between page linking and use of positive language, particularly with respect to innovative trends. Web pages of the same country domain form clusters of different size in the network that reveal strong correlations with sales market orientation. Our approach maintains the web content's hierarchical structure imposed by the web page networks. It, thus, presents a method to reveal hierarchical structures of unstructured text content obtained from web scraping. It is highly transparent, reproducible and data driven, and could be used to gain complementary insights into innovative strategies of firms and competitive landscapes, which would not be detectable by the analysis of web content alone.

cs.SI↗